update

Author: L1aoXingyu

chapter8_Application/Deep-Dream/backward/backward.py

@@ -1,13 +1,13 @@
-import torch as t
-from torch.autograd import Variable as v
+import torch
+from torch.autograd import Variable
 
 # simple gradient
-a = v(t.FloatTensor([2, 3]), requires_grad=True)
+a = Variable(torch.FloatTensor([2, 3]), requires_grad=True)
 b = a + 3
 c = b * b * 3
 out = c.mean()
 out.backward()
-print('*'*10)
+print('*' * 10)
 print('=====simple gradient======')
 print('input')
 print(a.data)
@@ -17,43 +17,44 @@
 print(a.grad.data)
 
 # backward on non-scalar output
-m = v(t.FloatTensor([[2, 3]]), requires_grad=True)
-n = v(t.zeros(1, 2))
-n[0, 0] = m[0, 0] ** 2
-n[0, 1] = m[0, 1] ** 3
-n.backward(t.FloatTensor([[1, 1]]))
-print('*'*10)
+m = Variable(torch.FloatTensor([[2, 3]]), requires_grad=True)
+n = Variable(torch.zeros(1, 2))
+n[0, 0] = m[0, 0]**2
+n[0, 1] = m[0, 1]**3
+n.backward(torch.FloatTensor([[1, 1]]))
+print('*' * 10)
 print('=====non scalar output======')
 print('input')
 print(m.data)
 print('input gradients are')
 print(m.grad.data)
 
 # jacobian
-j = t.zeros(2 ,2)
-k = v(t.zeros(1, 2))
+j = torch.zeros(2, 2)
+k = Variable(torch.zeros(1, 2))
 m.grad.data.zero_()
-k[0, 0] = m[0, 0] ** 2 + 3 * m[0 ,1]
-k[0, 1] = m[0, 1] ** 2 + 2 * m[0, 0]
-k.backward(t.FloatTensor([[1, 0]]), retain_variables=True)
+k[0, 0] = m[0, 0]**2 + 3 * m[0, 1]
+k[0, 1] = m[0, 1]**2 + 2 * m[0, 0]
+k.backward(torch.FloatTensor([[1, 0]]), retain_variables=True)
 j[:, 0] = m.grad.data
 m.grad.data.zero_()
-k.backward(t.FloatTensor([[0, 1]]))
+k.backward(torch.FloatTensor([[0, 1]]))
 j[:, 1] = m.grad.data
 print('jacobian matrix is')
 print(j)
 
 # compute jacobian matrix
-x = t.FloatTensor([2, 1]).view(1, 2)
-x = v(x, requires_grad=True)
-y = v(t.FloatTensor([[1, 2], [3, 4]]))
+x = torch.FloatTensor([2, 1]).view(1, 2)
+x = Variable(x, requires_grad=True)
+y = Variable(torch.FloatTensor([[1, 2], [3, 4]]))
 
-z = t.mm(x, y)
-jacobian = t.zeros((2, 2))
-z.backward(t.FloatTensor([[1, 0]]), retain_variables=True)  # dz1/dx1, dz2/dx1
+z = torch.mm(x, y)
+jacobian = torch.zeros((2, 2))
+z.backward(
+    torch.FloatTensor([[1, 0]]), retain_variables=True)  # dz1/dx1, dz2/dx1
 jacobian[:, 0] = x.grad.data
 x.grad.data.zero_()
-z.backward(t.FloatTensor([[0, 1]]))  # dz1/dx2, dz2/dx2
+z.backward(torch.FloatTensor([[0, 1]]))  # dz1/dx2, dz2/dx2
 jacobian[:, 1] = x.grad.data
 print('=========jacobian========')
 print('x')